Hacking scaladoc issue-8124.
Hacking scaladoc issue-8124. The user should ignore this object.
The signal-to-noise (S2N) metric ratio is a univariate feature ranking metric, which can be used as a feature selection criterion for binary classification problems.
The signal-to-noise (S2N) metric ratio is a univariate feature ranking metric, which can be used as a feature selection criterion for binary classification problems. S2N is defined as |μ_{1} - μ_{2}| / (σ_{1} + σ_{2}), where μ_{1} and μ_{2} are the mean value of the variable in classes 1 and 2, respectively, and σ_{1} and σ_{2} are the standard deviations of the variable in classes 1 and 2, respectively. Clearly, features with larger S2N ratios are better for classification.
The signal-to-noise (S2N) metric ratio is a univariate feature ranking metric, which can be used as a feature selection criterion for binary classification problems.
The signal-to-noise (S2N) metric ratio is a univariate feature ranking metric, which can be used as a feature selection criterion for binary classification problems. S2N is defined as |μ_{1} - μ_{2}| / (σ_{1} + σ_{2}), where μ_{1} and μ_{2} are the mean value of the variable in classes 1 and 2, respectively, and σ_{1} and σ_{2} are the standard deviations of the variable in classes 1 and 2, respectively. Clearly, features with larger S2N ratios are better for classification.
The ratio of between-groups to within-groups sum of squares is a univariate feature ranking metric, which can be used as a feature selection criterion for multi-class classification problems.
The ratio of between-groups to within-groups sum of squares is a univariate feature ranking metric, which can be used as a feature selection criterion for multi-class classification problems. For each variable j, this ratio is BSS(j) / WSS(j) = ΣI(y_{i} = k)(x_{kj} - x_{·j})^{2} / ΣI(y_{i} = k)(x_{ij} - x_{kj})^{2}; where x_{·j} denotes the average of variable j across all samples, x_{kj} denotes the average of variable j across samples belonging to class k, and x_{ij} is the value of variable j of sample i. Clearly, features with larger sum squares ratios are better for classification.
The ratio of between-groups to within-groups sum of squares is a univariate feature ranking metric, which can be used as a feature selection criterion for multi-class classification problems.
The ratio of between-groups to within-groups sum of squares is a univariate feature ranking metric, which can be used as a feature selection criterion for multi-class classification problems. For each variable j, this ratio is BSS(j) / WSS(j) = ΣI(y_{i} = k)(x_{kj} - x_{·j})^{2} / ΣI(y_{i} = k)(x_{ij} - x_{kj})^{2}; where x_{·j} denotes the average of variable j across all samples, x_{kj} denotes the average of variable j across samples belonging to class k, and x_{ij} is the value of variable j of sample i. Clearly, features with larger sum squares ratios are better for classification.
Feature generation, normalization and selection.
Feature generation (or constructive induction) studies methods that modify or enhance the representation of data objects. Feature generation techniques search for new features that describe the objects better than the attributes supplied with the training instances.
Many machine learning methods such as Neural Networks and SVM with Gaussian kernel also require the features properly scaled/standardized. For example, each variable is scaled into interval [0, 1] or to have mean 0 and standard deviation 1. Although some method such as decision trees can handle nominal variable directly, other methods generally require nominal variables converted to multiple binary dummy variables to indicate the presence or absence of a characteristic.
Feature selection is the technique of selecting a subset of relevant features for building robust learning models. By removing most irrelevant and redundant features from the data, feature selection helps improve the performance of learning models by alleviating the effect of the curse of dimensionality, enhancing generalization capability, speeding up learning process, etc. More importantly, feature selection also helps researchers to acquire better understanding about the data.
Feature selection algorithms typically fall into two categories: feature ranking and subset selection. Feature ranking ranks the features by a metric and eliminates all features that do not achieve an adequate score. Subset selection searches the set of possible features for the optimal subset. Clearly, an exhaustive search of optimal subset is impractical if large numbers of features are available. Commonly, heuristic methods such as genetic algorithms are employed for subset selection.